FIFA20 DATA ANALYSIS

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Importing the modules

modules are imported

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	...	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	158023	https://sofifa.com/player/158023/lionel-messi/...	L. Messi	Lionel Andrés Messi Cuccittini	32	1987-06-24	170	72	Argentina	FC Barcelona	...	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2
1	20801	https://sofifa.com/player/20801/c-ronaldo-dos-...	Cristiano Ronaldo	Cristiano Ronaldo dos Santos Aveiro	34	1985-02-05	187	83	Portugal	Juventus	...	65+3	61+3	61+3	61+3	65+3	61+3	53+3	53+3	53+3	61+3
2	190871	https://sofifa.com/player/190871/neymar-da-sil...	Neymar Jr	Neymar da Silva Santos Junior	27	1992-02-05	175	68	Brazil	Paris Saint-Germain	...	66+3	61+3	61+3	61+3	66+3	61+3	46+3	46+3	46+3	61+3
3	200389	https://sofifa.com/player/200389/jan-oblak/20/...	J. Oblak	Jan Oblak	26	1993-01-07	188	87	Slovenia	Atlético Madrid	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	183277	https://sofifa.com/player/183277/eden-hazard/2...	E. Hazard	Eden Hazard	28	1991-01-07	175	74	Belgium	Real Madrid	...	66+3	63+3	63+3	63+3	66+3	61+3	49+3	49+3	49+3	61+3

5 rows × 104 columns

(18278, 104)

['sofifa_id', 'player_url', 'short_name', 'long_name', 'age', 'dob', 'height_cm', 'weight_kg', 'nationality', 'club', 'overall', 'potential', 'value_eur', 'wage_eur', 'player_positions', 'preferred_foot', 'international_reputation', 'weak_foot', 'skill_moves', 'work_rate', 'body_type', 'real_face', 'release_clause_eur', 'player_tags', 'team_position', 'team_jersey_number', 'loaned_from', 'joined', 'contract_valid_until', 'nation_position', 'nation_jersey_number', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic', 'gk_diving', 'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed', 'gk_positioning', 'player_traits', 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure', 'defending_marking', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb']

Data Preprocessing

Dropping some useless columns

	short_name	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	...	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	L. Messi	32	170	72	Argentina	FC Barcelona	94	94	95500000	565000	...	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2
1	Cristiano Ronaldo	34	187	83	Portugal	Juventus	93	93	58500000	405000	...	65+3	61+3	61+3	61+3	65+3	61+3	53+3	53+3	53+3	61+3
2	Neymar Jr	27	175	68	Brazil	Paris Saint-Germain	92	92	105500000	290000	...	66+3	61+3	61+3	61+3	66+3	61+3	46+3	46+3	46+3	61+3
3	J. Oblak	26	188	87	Slovenia	Atlético Madrid	91	93	77500000	125000	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	E. Hazard	28	175	74	Belgium	Real Madrid	91	91	90000000	470000	...	66+3	63+3	63+3	63+3	66+3	61+3	49+3	49+3	49+3	61+3

5 rows × 95 columns

Calculating BMI

	short_name	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	...	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb	BMI
0	L. Messi	32	170	72	Argentina	FC Barcelona	94	94	95500000	565000	...	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2	24.913495
1	Cristiano Ronaldo	34	187	83	Portugal	Juventus	93	93	58500000	405000	...	61+3	61+3	61+3	65+3	61+3	53+3	53+3	53+3	61+3	23.735308
2	Neymar Jr	27	175	68	Brazil	Paris Saint-Germain	92	92	105500000	290000	...	61+3	61+3	61+3	66+3	61+3	46+3	46+3	46+3	61+3	22.204082
3	J. Oblak	26	188	87	Slovenia	Atlético Madrid	91	93	77500000	125000	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	24.615211
4	E. Hazard	28	175	74	Belgium	Real Madrid	91	91	90000000	470000	...	63+3	63+3	63+3	66+3	61+3	49+3	49+3	49+3	61+3	24.163265

5 rows × 96 columns

Player's Position

Converting the categorical values in Player's Position column in integer values.

	short_name	player_positions
0	L. Messi	RW, CF, ST
1	Cristiano Ronaldo	ST, LW
2	Neymar Jr	LW, CAM
3	J. Oblak	GK
4	E. Hazard	LW, CF
...	...	...
18273	Shao Shuai	CB
18274	Xiao Mingjie	CB
18275	Zhang Wei	CM
18276	Wang Haijian	CM
18277	Pan Ximing	CM

18278 rows × 2 columns

	Position_ CAM	Position_ CB	Position_ CDM	Position_ CF	Position_ CM	Position_ LB	Position_ LM	Position_ LW	Position_ LWB	Position_ RB	...	Position_GK	Position_LB	Position_LM	Position_LW	Position_LWB	Position_RB	Position_RM	Position_RW	Position_RWB	Position_ST
0	0	0	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	1	0	0
1	0	0	0	0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	0	1
2	1	0	0	0	0	0	0	0	0	0	...	0	0	0	1	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	1	0	0	0	0	0	0	0	0	0
4	0	0	0	1	0	0	0	0	0	0	...	0	0	0	1	0	0	0	0	0	0

5 rows × 29 columns

	short_name	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	...	Position_GK	Position_LB	Position_LM	Position_LW	Position_LWB	Position_RB	Position_RM	Position_RW	Position_RWB	Position_ST
0	L. Messi	32	170	72	Argentina	FC Barcelona	94	94	95500000	565000	...	0	0	0	0	0	0	0	1	0	0
1	Cristiano Ronaldo	34	187	83	Portugal	Juventus	93	93	58500000	405000	...	0	0	0	0	0	0	0	0	0	1
2	Neymar Jr	27	175	68	Brazil	Paris Saint-Germain	92	92	105500000	290000	...	0	0	0	1	0	0	0	0	0	0
3	J. Oblak	26	188	87	Slovenia	Atlético Madrid	91	93	77500000	125000	...	1	0	0	0	0	0	0	0	0	0
4	E. Hazard	28	175	74	Belgium	Real Madrid	91	91	90000000	470000	...	0	0	0	1	0	0	0	0	0	0

5 rows × 125 columns

	short_name	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	...	Position_GK	Position_LB	Position_LM	Position_LW	Position_LWB	Position_RB	Position_RM	Position_RW	Position_RWB	Position_ST
0	L. Messi	32	170	72	Argentina	FC Barcelona	94	94	95500000	565000	...	0	0	0	0	0	0	0	1	0	0
1	Cristiano Ronaldo	34	187	83	Portugal	Juventus	93	93	58500000	405000	...	0	0	0	0	0	0	0	0	0	1
2	Neymar Jr	27	175	68	Brazil	Paris Saint-Germain	92	92	105500000	290000	...	0	0	0	1	0	0	0	0	0	0
3	J. Oblak	26	188	87	Slovenia	Atlético Madrid	91	93	77500000	125000	...	1	0	0	0	0	0	0	0	0	0
4	E. Hazard	28	175	74	Belgium	Real Madrid	91	91	90000000	470000	...	0	0	0	1	0	0	0	0	0	0

5 rows × 124 columns

Positioning Columns ratings

Cleaning, Processing and Assigning the new attributes to columns listed below.

	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	...	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	89+2	89+2	89+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	...	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2
1	91+3	91+3	91+3	89+3	90+3	90+3	90+3	89+3	88+3	88+3	...	65+3	61+3	61+3	61+3	65+3	61+3	53+3	53+3	53+3	61+3
2	84+3	84+3	84+3	90+3	89+3	89+3	89+3	90+3	90+3	90+3	...	66+3	61+3	61+3	61+3	66+3	61+3	46+3	46+3	46+3	61+3
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	83+3	83+3	83+3	89+3	88+3	88+3	88+3	89+3	89+3	89+3	...	66+3	63+3	63+3	63+3	66+3	61+3	49+3	49+3	49+3	61+3

5 rows × 26 columns

omitting the '+' sign

	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	...	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	89	89	89	93	93	93	93	93	93	93	...	68	66	66	66	68	63	52	52	52	63
1	91	91	91	89	90	90	90	89	88	88	...	65	61	61	61	65	61	53	53	53	61
2	84	84	84	90	89	89	89	90	90	90	...	66	61	61	61	66	61	46	46	46	61
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	83	83	83	89	88	88	88	89	89	89	...	66	63	63	63	66	61	49	49	49	61
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18273	32	32	32	31	31	31	31	31	31	31	...	43	42	42	42	43	45	46	46	46	45
18274	33	33	33	33	32	32	32	33	33	33	...	44	43	43	43	44	46	47	47	47	46
18275	43	43	43	43	43	43	43	43	44	44	...	47	49	49	49	47	47	49	49	49	47
18276	43	43	43	45	44	44	44	45	46	46	...	48	48	48	48	48	48	49	49	49	48
18277	42	42	42	44	43	43	43	44	46	46	...	48	49	49	49	48	48	50	50	50	48

18278 rows × 26 columns

Replacing NaN values with 0

Converting the columns to int

Checking the dataframe again

	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	...	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	89	89	89	93	93	93	93	93	93	93	...	68	66	66	66	68	63	52	52	52	63
1	91	91	91	89	90	90	90	89	88	88	...	65	61	61	61	65	61	53	53	53	61
2	84	84	84	90	89	89	89	90	90	90	...	66	61	61	61	66	61	46	46	46	61
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	83	83	83	89	88	88	88	89	89	89	...	66	63	63	63	66	61	49	49	49	61
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18273	32	32	32	31	31	31	31	31	31	31	...	43	42	42	42	43	45	46	46	46	45
18274	33	33	33	33	32	32	32	33	33	33	...	44	43	43	43	44	46	47	47	47	46
18275	43	43	43	43	43	43	43	43	44	44	...	47	49	49	49	47	47	49	49	49	47
18276	43	43	43	45	44	44	44	45	46	46	...	48	48	48	48	48	48	49	49	49	48
18277	42	42	42	44	43	43	43	44	46	46	...	48	49	49	49	48	48	50	50	50	48

18278 rows × 26 columns

Filling Missing Values

filling "dribbling", "defending", "physic", "passing", "shooting" and "pace" missing values of these columns by median

	dribbling	defending	physic	passing	shooting	pace
0	96.0	39.0	66.0	92.0	92.0	87.0
1	89.0	35.0	78.0	82.0	93.0	90.0
2	95.0	32.0	58.0	87.0	85.0	91.0
3	NaN	NaN	NaN	NaN	NaN	NaN
4	94.0	35.0	66.0	86.0	83.0	91.0
...	...	...	...	...	...	...
18273	33.0	47.0	51.0	28.0	23.0	57.0
18274	35.0	48.0	48.0	33.0	24.0	58.0
18275	45.0	48.0	51.0	44.0	35.0	54.0
18276	47.0	45.0	52.0	47.0	35.0	59.0
18277	45.0	47.0	55.0	51.0	32.0	60.0

18278 rows × 6 columns

dribbling    2036
defending    2036
physic       2036
passing      2036
shooting     2036
pace         2036
dtype: int64

	dribbling	defending	physic	passing	shooting	pace
0	96.0	39.0	66.0	92.0	92.0	87.0
1	89.0	35.0	78.0	82.0	93.0	90.0
2	95.0	32.0	58.0	87.0	85.0	91.0
3	64.0	56.0	66.0	58.0	54.0	69.0
4	94.0	35.0	66.0	86.0	83.0	91.0
...	...	...	...	...	...	...
18273	33.0	47.0	51.0	28.0	23.0	57.0
18274	35.0	48.0	48.0	33.0	24.0	58.0
18275	45.0	48.0	51.0	44.0	35.0	54.0
18276	47.0	45.0	52.0	47.0	35.0	59.0
18277	45.0	47.0	55.0	51.0	32.0	60.0

18278 rows × 6 columns

short_name      0
age             0
height_cm       0
weight_kg       0
nationality     0
               ..
Position_RB     0
Position_RM     0
Position_RW     0
Position_RWB    0
Position_ST     0
Length: 124, dtype: int64

Exploratory Data Analysis

1- Scatter Plot (colored by Age) year 2020 - Overall Rating vs Value in Euros

2- Pie chart proportion of right-foot players vs left-foot players

3- Histogram of Players Ages

4- Scatterpolar plot to compare a player's grothw over time

Loading the other datasets players from 2016 to 2019

player attributes column names

Creating a method to compare a Players growth over Time

Let's check the growth of Neymar over time

what about Cristiano Ronaldo

6- Pie chart Describing the Percentage of Players in different Attacker positions

7- Pie chart Describing the Percentage of Players in different Midfielder positions

8- Pie chart Describing the Percentage of Players in different Defender positions

Pick Top 5 Players per Position

Creating a method to pick top 5 player based on a the player position and the player value in euro

	short_name	age	overall	value_eur
101	Marcelo	31	85	28000000
103	Alex Sandro	28	85	33000000
124	Alex Telles	26	84	33000000
174	Grimaldo	23	83	29500000
179	L. Digne	25	83	28500000

	short_name	age	overall	value_eur
19	L. Suárez	32	89	53000000
34	E. Cavani	32	88	47000000
71	C. Immobile	29	86	44500000
72	A. Lacazette	28	86	46000000
89	R. Lukaku	26	85	46000000

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